Next week is @AIconference and the Chalk team is ready to show up big. Our co-founder @marx_elliot will be presenting "Two Halves of Inference to Build, Eval & Run Agents" on 10/1 at 1pm PT. If you struggle with stale data and evals that can’t predict production performance, this is the talk for you. Plus, the Chalk team will be hanging out on the exhibit floor throughout the conference. Stop by Booth 262 to chat with our FDEs, catch live demos, and grab some of our latest Chalk swag. Schedule time with us in advance by visiting the link below. 👇

Better agents require better evals. Better evals require better context. Most agent evals are broken before they even start. Not because the model reasoned poorly, but because it was scored on a prompt built from stale, disconnected data. That's not a model problem. That’s a data assembly failure. The fix? A unified context layer that delivers real-time data at inference. Next month our Co-Founder Elliot Marx will host a live webinar to show you how to turn your data into actionable context for your agents, so you can run proper evals and deploy with confidence. October 8th at 10am PT/1pm ET. Register at the link below.

What can a hand of poker teach us about how real-time context changes agent behavior? Our Developer Advocate @rkundy built a poker agent to show how recommendations change with fresh data vs historical data alone. First presented at the @temporalio's 'Durable AI' meetup, Rishi now shares his learnings in our new blog post, "Context Has a Timestamp". Link in the comments.

“If we can make context and features the same, you eliminate drift and have canonical ways of doing things at your company.” Your agents and models need fresh data. A context engine provides the data foundation that gives you speed without sacrificing data freshness. Our co-founder, @marx_elliot , walks through how a real-time context layer enables your data teams to define the features and real-time signals your models run on, and then delivers that same context directly to your agents at inference. Full video at the link below.

We're heading to @AIconference. One month out, and the team is getting the booth, demos, and swag ready. Find us at Booth 262 on Pier 48 throughout the conference, and tune in to the talk from our Co-Founder @marx_elliot on 10/1 at 1pm PT in Theater 2. We'll be showcasing how Chalk provides the data and infrastructure solutions to deliver real-time context to models and agents, fine-tune LLMs, and evaluate agents in production. Attending the conference? Schedule time to meet with us at the link in the comments.

Your agent doesn't fail because the model is wrong. It fails because the data wasn't there when it needed it. Stale, ungoverned, or missing at the moment the decision had to happen. Getting that right is a stack decision, and you make it once. Applications are open for Chalk for Startups, for teams assembling that stack for the first time. Accepted teams get: - An architecture review with our engineers before you build. Your repo, pull requests against your code - A scoped first workload with a plan to production - Preferred pricing - Amplification when you launch or raise, plus a founder network of technical teams Apply if AI or ML is core to what you're building, you've raised Seed, Series A or B, and you're new to Chalk. Link in the first comment.

"We want to be able to achieve continuous improvement, that's always been the goal of machine learning." Learn how you can continuously improve your ML models through dataset generation, training, and controlled experimentation, in this on-demand webinar from our co-founder @marx_elliot. Link below.